Fungal diversity, surveillance and incidence forecasting of crop-associated phytopathogens using metabarcoding of aerial spore and suction traps

Citation

Tremblay, É.D., Goulet, B.B., Lord, É., Samson, S., Liu, M., Brunet, B.M.T., and Parent, J.-P. (2023). Fungal diversity, surveillance and incidence forecasting of crop-associated phytopathogens using metabarcoding of aerial spore and suction traps. In The Canadian Tri-Society meeting (Ottawa, ON, Canada)

Plain language summary

Plant pathogens present a risk to the health of crops, timber and other economically important plant derived commodities. Occasionally introduced through international trade, some plant pathogens and pests can become a threat to biodiversity. These introductions are more and more frequent and aggravated by climate change.
Their emergence in agricultural environments can lead to more restrictive international trade regulations.
A rapid response needs to be established to mitigate the consequences. High-throughput sequencing (HTS) technologies can be used to assess the distribution and incidence of plant pathogens. The diversity of airborne spores of fungi and oomycetes in agricultural fields from the provinces of Quebec and Ontario was studied. Aerial spore and suction traps were used. An artificial intelligence (AI)-based model is being developed to forecast such incidence based on weather factors. Preliminary results will be presented. The combination of sequencing technologies and AI was used as a pre-screening tool to improve risk readiness and response at a large scale.

Abstract

Phytopathogens present a risk to the health of crops, seeds, timber and other economically important plant derived commodities. Occasionally introduced through international trade, some plant pathogens and pests can threaten the biodiversity associated with native plantsʼ and beneficial microorganismsʼ. The rate of these introductions is accelerating as climate change creates more permissive conditions in areas previously protected by inhospitable conditions. Increased presence and the emergence of new phytopathogens in agricultural environments may also lead to more restrictive international trade regulations and therefore a rapid response needs to be established to mitigate the consequences. Metabarcoding is a powerful high-throughput sequencing (HTS) approach to scrutinize the distribution and incidence of phytopathogens and locate emerging speciesʼ hotspots. The diversity of environmental fungi and oomycetes in agricultural fields from the provinces of Quebec and Ontario was assessed using samples collected with aerial spore and suction traps. An artificial intelligence-based model using a Graph Neural Network (GNN) is being developed to forecast phytopathogen incidence based on weather factors, climate change and geographical locations. Preliminary results will be presented. Application of metabarcoding and artificial intelligence-based modeling methods as a pre-screening tool could be instrumental to improve phytopathogen risk readiness and response at a larger scale than highly-specific but low-throughput and time-consuming classical methods, and to increase our understanding of pest incidence and transmission across space and time.